Robust parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library (1409.7215v2)

Abstract: The Advanced LIGO and Advanced Virgo gravitational wave (GW) detectors will begin operation in the coming years, with compact binary coalescence events a likely source for the first detections. The gravitational waveforms emitted directly encode information about the sources, including the masses and spins of the compact objects. Recovering the physical parameters of the sources from the GW observations is a key analysis task. This work describes the LALInference software library for Bayesian parameter estimation of compact binary signals, which builds on several previous methods to provide a well-tested toolkit which has already been used for several studies. We show that our implementation is able to correctly recover the parameters of compact binary signals from simulated data from the advanced GW detectors. We demonstrate this with a detailed comparison on three compact binary systems: a binary neutron star, a neutron star black hole binary and a binary black hole, where we show a cross-comparison of results obtained using three independent sampling algorithms. These systems were analysed with non-spinning, aligned spin and generic spin configurations respectively, showing that consistent results can be obtained even with the full 15-dimensional parameter space of the generic spin configurations. We also demonstrate statistically that the Bayesian credible intervals we recover correspond to frequentist confidence intervals under correct prior assumptions by analysing a set of 100 signals drawn from the prior. We discuss the computational cost of these algorithms, and describe the general and problem-specific sampling techniques we have used to improve the efficiency of sampling the compact binary coalescence parameter space.

• The paper presents a comprehensive Bayesian framework employing MCMC, nested sampling, and BAMBI techniques to improve parameter estimation for compact binaries.
• It rigorously validates the LALInference toolkit using simulated signals from binary neutron stars, neutron star-black hole, and binary black hole systems in realistic noise.
• The study demonstrates enhanced computational efficiency and scalability, ensuring reliable astrophysical parameter recovery for prompt gravitational-wave analyses.

Parameter Estimation for Compact Binaries with LALInference

The paper discusses essential advancements in parameter estimation for compact binaries using gravitational-wave (GW) data. The authors focus on the LALInference software toolkit, which is integral to analyzing signals from sources such as binary neutron stars (BNS), neutron star-black hole binaries (NSBH), and binary black holes (BBH). Given the advancements in GW detectors, like Advanced LIGO and Advanced Virgo, there is an imperative need for robust methods that can extract meaningful astrophysical parameters from the data captured by these instruments.

Methodological Overview

LALInference provides a Bayesian framework for parameter estimation, capitalizing on the well-established technique of Markov Chain Monte Carlo (MCMC) as well as nested sampling approaches. The toolkit contains implementations from different algorithms: an MCMC-based implementation, a conventional nested sampling algorithm (LALInferenceNest), and a MultiNest approach via the BAMBI algorithm. Each of these implementations is designed to address different challenges within the parameter spaces, including high dimensionality and complex likelihood surfaces driven by the intrinsic and extrinsic parameters of the merging systems.

Central to the Bayesian approach is the use of prior distributions, which in this paper encompass various physical properties, including component masses, spins, sky localization, and inclination angles. The authors describe methodologies for analytically marginalizing over nuisance parameters such as the orbital phase to enhance computational efficiency.

Numerical Results and Performance Evaluation

The paper reports numerical experiments validating LALInference on synthetic GW signals injected into Gaussian noise representative of typical detector environments. Three vital simulated scenarios are assessed: a BNS, an NSBH, and a BBH. The different samplers were evaluated for their ability to accurately recover mass parameters, spin magnitudes, and configuration angles, among others. The comparative analysis between the MCMC, Nested Sampling, and BAMBI techniques demonstrated that all methods produced consistent and reliable posteriors, validating their use across different systems and complexity levels.

Performance evaluations further highlight computational aspects and efficiency, considering the waveform generation cost under different approximants. The suite was tested rigorously to ensure scalability and adaptability for various waveform models, proving its efficacy in achieving low computational latencies, essential for prompt parameter estimation following a detection.

Implications and Future Prospects

Practically, the development of LALInference is significant for its modularity and adaptability to various waveform models, which will allow astrophysicists to harness the full potential of incoming detector data. The framework lays groundwork for immediate application in scenarios requiring quick analysis — such as coincident electromagnetic observations — and will be pivotal in forthcoming observation runs as more sensitive networks come online.

Theoretically, this paper enhances our understanding of the effects of parameter degeneracies on GW data analysis, guiding improvements in Bayesian inference techniques. It also facilitates future endeavors in model selection and testing general relativity with GW data, which are crucial for advancing fundamental astrophysical research.

Looking forward, the integration of adaptive noise models and the pursuit of reduced-order modelling promise to further increase the reliability and speed of parameter estimation, amplifying the scientific return of GW observations. These developments underscore the strategic enhancement of LALInference to meet evolving data analysis needs in the field of gravitational-wave astronomy.